GEOGRAPHIC INFORMATION -BASED ROUTING ...3.1.1 Routing problems A WSN with a vast land area has few obstacles between deployed nodes, and cluster-based security protocols mainly use

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 3, June 2018

DOI: 10.5121/ijwmn.2018.10302 13

GEOGRAPHIC INFORMATION-BASED ROUTING

OPTIMIZATION USING GA FOR CLUSTER-BASED

WSNS

Sanghyeok Lim1 and Taeho Cho

2

1College of Information and Communication Engineering, Sungkyunkwan University,

Republic of Korea

2College of Software, Sungkyunkwan University, Republic of Korea

ABSTRACT

Wireless sensor networks are used for data collection and event detection in various fields such as home

networks, military systems, and forest fire monitoring, and are composed of many sensor nodes and a base

station. Sensor nodes have limited computing power, limited energy, are randomly distributed in an open

environment that operates independently, and have difficulties in individual management. Taking

advantage of those weaknesses, attackers can compromise sensor nodes for various kinds of network

attacks. Several security protocols have been proposed to prevent these attacks. Most of the security

protocols form routings with cluster head nodes. In the case of routing using only cluster head nodes, it is

difficult to re-route when the size of the cluster is increased or the number of the surviving nodes is reduced.

To prevent these attacks, the proposed scheme maintains security in a cluster-based security protocol and

shows energy efficient routing using genetic algorithm by selecting the appropriate cluster head nodes and

utilizing the characteristics of the sensor node with different transmission outputs based on the distance

between each node. In this paper, we use a probabilistic voting-based filtering scheme, one of the cluster-

based security protocols, and the shortest path, which is a hierarchical routing protocol that the original

probabilistic voting-based filtering scheme is using, to test the proposed scheme. This experiment shows the

performance comparison of the routing success rate and routing cost according to the number of nodes on

the field, as well as the performance comparison according to the cluster size per number of nodes.

KEYWORDS

Network Protocols, Wireless Sensor Network, Network attack, Genetic algorithm, Routing

1. INTRODUCTION

Wireless sensor networks (WSNs) are used for data collection and event detection in various

fields, such as home networks, military systems, and forest fire monitoring, and are composed of

many sensor nodes and a base station (BS) [1], [2]. When an event occurs, the sensor node detects

the event, makes a report of that event and sends it over multiple hops of the sensor nodes to the

BS. However, sensor nodes are vulnerable to attack because of the disadvantages of limited

computation, limited energy, random distribution in an open environment that operates

independently, and difficulties in individual management. An attacker exploits these weaknesses

to compromise nodes and perform various kinds of attacks. These attacks reduce the energy of the

node, shorten the lifetime of the network, and prevent the detection and reporting of normal

events. Several protocols have been proposed to increase the security and lifetime of the network

by defending against such attacks [3], [4], [5], [6], [21]. These protocols form cluster-based

routing, which benefits from local management of nodes and it makes systemic report generation

possible. However, such cluster-based routing, which relies only on CH nodes for report

transmission and verification, has a significant negative impact on security considering the entire

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No.

network because of various attacks

downstream event detection cluster

most WSN fields, the node with the shortest distance to the BS and within the transmission range

based on the report delivery node is selected as the next transfer node.

of nodes is appropriately placed in the field

reasonable cost.

However, if the number of nodes placed in the field is insufficient or the size of the cluster is

inevitably increased due to a change

is likely to be reduced, and it becomes difficult to guarant

increases the total number of routing hops, thereby reducing the lifetime of the network.

to prevent such problems, the CHs of each event detection cluster perform

report generation, and report transmission.

among all the nodes with an appropriate geographical advantage considering the distance to the

BS and transmission power. The existing layer

security protocols implement routing

distance between nodes [7], [8]

different transmission output levels according to

the routing is constructed using

setting the appropriate output level for all nodes forming the routing.

distance of the receiving node from the initial event detection node, the

the node with the smallest distance from the BS

existing protocol randomly select the CH or select the n

experiments, we confirmed that the performance difference between the proposed scheme and the

existing scheme increases as the number of nodes decreases.

scheme based on PVFS which is

routing.

2. RELATED WORK

2.1. PVFS

International Journal of Wireless & Mobile Networks (IJWMN) Vol. 10, No. 3, June

attacks, such as sniffing or dropping reports transmitted from the

event detection cluster. Additionally, considering the shortest path that is applied to

, the node with the shortest distance to the BS and within the transmission range

based on the report delivery node is selected as the next transfer node. When a sufficient number

ropriately placed in the field, this ensures stable routing formation and


change in the field environment, the forwarding node candidate list

it becomes difficult to guarantee energy efficient routing

increases the total number of routing hops, thereby reducing the lifetime of the network.

to prevent such problems, the CHs of each event detection cluster perform only event detec

and report transmission. The verification node is considered

an appropriate geographical advantage considering the distance to the

The existing layer-based routing schemes applied to most

security protocols implement routing sets maximum transmission output without regard to the

], [8]. Micaz mote used in most of the security protocols

different transmission output levels according to each node’s distance [9]. In the proposed scheme,

the routing is constructed using genetic algorithm (GA) to minimize the cost of the routing by

setting the appropriate output level for all nodes forming the routing. In addition, to minimize the

ing node from the initial event detection node, the CH node was selected as

the node with the smallest distance from the BS, thereby saving the initial routing cost while

randomly select the CH or select the node with the smallest ID valu


existing scheme increases as the number of nodes decreases. We experimented

is one of a cluster-based security protocol using the

Figure 1. PVFS’s en-route filtering.

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or dropping reports transmitted from the

the shortest path that is applied to

, the node with the shortest distance to the BS and within the transmission range

a sufficient number

ensures stable routing formation and a


forwarding node candidate list

routing. This routing

increases the total number of routing hops, thereby reducing the lifetime of the network. In order

event detection,

The verification node is considered and selected

an appropriate geographical advantage considering the distance to the

applied to most of the

without regard to the

protocols can set

In the proposed scheme,

to minimize the cost of the routing by

In addition, to minimize the

node was selected as

the initial routing cost while

ode with the smallest ID value. Through


our propose

shortest path


15

PVFS is a typical cluster-based security protocol. Nodes randomly placed on the field are

assigned to the same cluster within an appropriate range to detect events within the same cluster

area. At the end of the node deployment, PVFS performs network security and report

transmission in four steps.

� Key distribution phase

The BS transmits to each CH a set of keys separated by a cluster size. The CH that receives the

key set distributes one key to each member node.

� Selection step of the verification node

The verification node is selected by the probability based on the number of hops between the

event detection cluster and the BS, and the number of hops between the node and BS among the

CHs on the path. The CH selected as the verification node receives one of the keys of the member

nodes of the event detection cluster at random. The CH of the selected cluster in the verification

node clears all keys except its own in its own key set.

� Report delivery phase

After detecting the event, the CH generates a report on the event, receives the MAC created by

the member nodes of each node, and attaches it to the report to generate the final report.

� Verification step

When the verification node receives the report, it first compares its own key index with the key

index of the MAC attached to the report. If the key index overlaps, the MAC check is performed.

If the index does not overlap, the report is not verified and sent to the node on the next path. If a

normal MAC is detected as a result of the check, a normal vote is cast. If a false MAC is detected,

a false vote is cast. If the number of votes reaches a preset threshold value, the report is

discriminated as normal or false. A report that is deemed to be a normal report is then

immediately directed to the BS without any further verification steps.

2.2. Genetic Algorithm

The genetic algorithm (GA) was proposed by Holland John and takes ideas from evolutionary

processes occurring in the natural world. This kind of algorithm represents solutions to a problem

as bit strings and finds an optimal solution through evolution [10], [22], [23]. The GA unit

probabilistically selects two individuals constituting the current generation. The selection

probability for each individual is proportional to the fitness of the individual, and the child

generations consist of individuals with greater fitness than the members of the parent generation.

Selected individuals generate a new generation through child generating, mutating, and

sometimes elite choosing [11], [12]. To solve the problem of local fixation in evolution the

proposed GA uses mutation technic.

3. PROPOSED METHOD

3.1. Problem statement

3.1.1 Routing problems

A WSN with a vast land area has few obstacles between deployed nodes, and cluster-based

security protocols mainly use the shortest path based routing. In a highly selective environment,


16

this is efficient for the forwarding node in the routing formation. However, in many security

protocols, including PVFS, the selection range of the forwarding node is limited to the CH among

all nodes. Moreover, once the CH is selected, it will not be replaced by any other member node.

In the situation where the CH is being used for extortion or because of battery discharge, the

corresponding routing scheme shows a significant weakness in re-routing. In some cases, when

the next forwarding node is set based only on the distance to the BS, the selection of the

geographically optimized node cannot be performed, which results in the adverse effect of the

increased number of routing hops.

3.1.2 Delivery node selection problem within cluster-based security protocols

Most of the cluster-based protocols select only those nodes that are responsible for report delivery

among the CH nodes. There are two bottlenecks in the routing path: key storage and energy

consumption. The key storage bottleneck is a phenomenon in which the number of keys owned

by the CH for the verification of downstream nodes increases. When these CHs are compromised,

the attacker will have multiple keys and can cause a significant security problem. The bottleneck

of energy consumption is the phenomenon that the routing of the downstream nodes to the BS

proceeds through the corresponding CH. As the number of reports to be transferred to the CH

increases exponentially, the expectation of energy consumption can be greater than other nodes

and the battery will be discharged soon. The network will then have to go through the routing

again and additional energy consumption is expected.

3.1.3 Uniform output power of sensor nodes

In the existing security protocols, all nodes transmit the report to its upstream nodes with a

maximum transmission range. In actuality, it is unnecessary to communicate only with the

maximum transmission distance by disregarding the interval between each sensor node. It is

therefore useful to measure the distance between nodes and transmit them at the appropriate

output level. The distance measurement between nodes can be implemented using the RSSI value

[13], or the node attached GPS module [14]. The Micaz node, which is often used in WSNs, is a

node that can control the output of a total of 32 levels, allowing the user to set the appropriate

output value [15], [16], [17]. There are many experiments on the transmission distance according

to the output in actual situations [18], [19], [20].

3.2. System overview

The experiment of the proposed method constitutes the system based on PVFS. All key

distribution and en-route filtering procedures follow existing security protocols. After the node is

deployed, each node sends its location information to the BS. Based on this information, the BS

selects the nodes with the shortest distances from the BS itself in each cluster as CH. The

difference in geographical location in the selection of CH has no effect on the security of the

protocol. This is because in the existing security protocol, CH selection was made without taking

any other factors into account. The key distribution and verification node selection phase follows

the original protocol’s phase. Then, the intermediate delivery node is randomly selected among

all the nodes, including the cluster head node. The selection of the next forwarding node is based

on the nodes within the maximum transmission range of itself and re-divides the range based on

the distance to the BS among the nodes within its transmission range.


Figure 2. Range of the candidate list of the forwarding node

In Figure 2, (a), (b), and (c) are the range

nodes can be selected by the downstream

diversity of the nodes forming the routing.

information overlapping between parent chromosomes in

a next generation node having a cross

range of the forwarding node will be explained


ange of the candidate list of the forwarding node.

and (c) are the ranges of the candidate list of the forwarding node.

downstream node with a larger range, resulting in an increase in the

the nodes forming the routing. However, this means that there is less genetic

information overlapping between parent chromosomes in the GA, and it is also difficult to create

a cross-point between parents. The contents of the candidate list

will be explained after dealing with the routing through the GA.

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of the candidate list of the forwarding node. More

, resulting in an increase in the

However, this means that there is less genetic

difficult to create

The contents of the candidate list

after dealing with the routing through the GA.


Figure

The GA chromosome contains the IDs of the forwarding nodes

chromosome is equal to the number of hops in each routing

is inversely proportional to the final

of the two parental chromosomes selected do not overlap, they will not have a cross

case, the parent chromosome is re

parent chromosome is large, the probability that the genetic information of each other does not

overlap increases. The proposed scheme limits the re

genetic information overlaps. When the number of re

chromosomes with the highest fitness are re

a modified version of the elitism technique used in genetic algorithms.

changed the size of the node list to increas

Additionally, we did not use the elitism technique

of the candidate list of the transfer node of

chromosome generating ability of the GA and

to re-select the routing within a short time, the scope of the candidate list can be narrowed and the

number of GA routines can be reduced to form an immediate routing

greater. Conversely, if the user wants to optimize the routing cost,

of the list and increase the number of repetitions of the GA.


Figure 3. Routing optimization using the GA.

The GA chromosome contains the IDs of the forwarding nodes, and the length of each

number of hops in each routing path. The fitness of the chromosomes

is inversely proportional to the final routing cost of the chromosomes. If the genetic information

of the two parental chromosomes selected do not overlap, they will not have a cross-

case, the parent chromosome is re-selected through the same method. If the candidate to be the


The proposed scheme limits the re - election of parental chromosomes until

When the number of re-elections reaches a predetermined threshold,

chromosomes with the highest fitness are re-elected and transferred to the next generation

a modified version of the elitism technique used in genetic algorithms. Instead of

list to increase the diversity of chromosomes.

, we did not use the elitism technique that causes the fixation phenomenon.

of the candidate list of the transfer node of Figure 2 has a trade-off between the child

ability of the GA and its routing cost. Therefore, if the network user has

select the routing within a short time, the scope of the candidate list can be narrowed and the

number of GA routines can be reduced to form an immediate routing, even if the cost is slightly

Conversely, if the user wants to optimize the routing cost, the user can increase the range

number of repetitions of the GA.

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and the length of each

The fitness of the chromosomes

If the genetic information

-point. In this

If the candidate to be the


election of parental chromosomes until

hes a predetermined threshold,

elected and transferred to the next generation. This is

Instead of mutation, we

causes the fixation phenomenon. The size

off between the child

Therefore, if the network user has

select the routing within a short time, the scope of the candidate list can be narrowed and the

if the cost is slightly

can increase the range


Figure 4 shows the original routing

Route # 2 have two cross-points. These are routing information for

respectively. Thus, the child chromosome starting from

chromosome at the cross-point and forms the routing.

hops and less energy usage, and in

generation is repeated, the child route picks the optimal interval of the

eventually has less routing costs and higher expectations than the

and routing is then completed.

4. EXPERIMENTAL RESULT

4.1. Experimental environment

4.2. Assumptions

The transmission and reception costs are set to 16.25

calculated cost of voting was set to 15

nodes, which can select 32 distinct


Figure 4. Route comparison.

original routing and modified routing using proposed scheme. R

points. These are routing information for Parent # 1 and

Thus, the child chromosome starting from Route # 1 changes the path of the parent

point and forms the routing. In Section A, Parent #1 routing has fewer

d less energy usage, and in Section B, Parent # 2 routing is more efficient.

generation is repeated, the child route picks the optimal interval of the parent appropriately and

eventually has less routing costs and higher expectations than the parent generation in this way

ESULT

Experimental environment

Table1. Experiment parameters

The transmission and reception costs are set to 16.25µj and 12.5µj, respectively, and the

calculated cost of voting was set to 15µj. In our experiment, Micaz motes were used as sensor

distinct output levels [9]. Experimental results show that the proposed

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Route # 1 and

and Parent # 2,

oute # 1 changes the path of the parent

1 routing has fewer

2 routing is more efficient. As the

parent appropriately and

parent generation in this way

j, respectively, and the

were used as sensor

Experimental results show that the proposed


20

method does not adjust the maximum output level as the conventional method does, but instead

the proposed method adjusts the maximum output level. Since the report size of the proposed

PVFS and the proposed protocol are the same, the transmission failure is not based on the size of

the report to be transmitted. To increase the reliability and realism of the experiment, experiments

were conducted based on the maximum transmittable distance table according to the node output

level. In the experiment of the proposed method, the list size is 70% of the maximum transfer

range and the number of households is set to not exceed 50 generations. Rather than focusing on

reducing routing costs, we focused on speeding up the routing updates. If the cost is less than the

existing routing, the method is applied to the network immediately without entering the next

generation so the routing can be formed more promptly.

4.3. Experimental result

Figure 5. Number of routing formation successes.

Figure 5 shows the number of routing formation successes according to the number of nodes

placed in the field. Overall, the difference in successes between the existing routing scheme and

the proposed routing scheme is approximately 25%. It is possible to confirm the consistency of

the routing formation success rate, irrespective of the number of nodes. In the conventional

scheme, regardless of the number of nodes, the node with the smallest ID in one cluster is

selected as CH, and thus does not affect the routing formation. In the case of the proposed scheme,

even if the number of nodes is reduced, the performance is constant because the number of nodes

is still high.

0

20

40

60

80

100

120

140

160

180

2000 1800 1500 1200 1000

N

u

m

b

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r

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f

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PVFS Proposed Scheme


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Figure 6. Routing costs depending on the number of nodes on the field.

Figure 6 shows a comparison of the routing costs consumed by each protocol depending on the

number of nodes on the field. Comparisons were made only among successful routing cases. In

the conventional scheme, energy consumption is independent of the number of nodes, and the

number of nodes and routing cost are not related. In the case of the proposed method, as the

number of generations of GA increases, the energy consumption gradually increases. However, in

the experiment, the creation of the child chromosome for finding a better route path occurs

immediately when the energy consumption is lower than that of the conventional method. In the

energy consumption comparison experiment, the comparison with existing PVFS without the

output level difference application was performed. Moderating the output of the node shows

twice as much energy savings. In existing and proposed schemes with different output levels, the

overall cost difference is approximately 20% due to the location correction of the event detection

CH and the optimization of the routing cost optimization.

0

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2000 1800 1500 1200 1000

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PVFS PVFS2 Proposed Scheme


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Figure 7. Number of routing successes according to the cluster size.

Figure 7 shows the number of routing successes according to the cluster size. When the cluster

size is relatively small, there is not a large difference in the number of routing successes between

the two schemes. However, as the size of the cluster increases, the number of routing successes

increases. The number of successes according to the size of the existing scheme was greatly

reduced because a node that does not reach the transmission range frequently occurs due to the

randomly set CH. Alternatively, the proposed method does not have a large drop in selectivity of

the forwarding node in the entire cluster, not just the CH. Additionally, because the algorithm is

focused on immediate routing rather than routing costs, it is expected to be less if the system is set

for cost reduction purposes.

Figure 8. Difference in routing costs according to cluster size.

100

150

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40 43 45 47 50 53 55 57 60

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PVFS Proposed Scheme

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40 43 45 47 50 53 55 57 60

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Size of clusters

PVFS PVFS2 Proposed Scheme


23

Figure 8 shows the difference in routing costs according to cluster size. This is similar to the

figure of the routing cost according to the number of nodes, but the cluster size affects the entire

routing success failure and can be interpreted as independent of the routing cost. Routing

optimization of each node was completed without exceeding 10 generations. Routing of all CHs

took less than 1 minute based on the field where more than 2000 nodes were dispatched.

5. CONCLUSIONS

Conventional cluster-based protocols are characterized by routing proceeding through CHs only,

and the dependency on the usability of the CHs is so high that if the CHs are compromised, the

security and energy efficiency of the entire routing using the nodes are damaged. Additionally,

the higher energy usage of CH is expected when it is close to the BS. In this case, faster battery

depletion occurs within those CHs, and the network users have to reroute frequently. In addition,

there is also unnecessary energy consumption, which does not reflect the distance between nodes,

but makes all transmissions the maximum output level. To solve these problems, the proposed

scheme selects CH based on regional suitability, and selects proper delivery nodes considering

every node in the network field. Cost optimization of routing was performed using the GA to

optimize hop count and communication distance. With a proposed scheme, optimization of the

routing cost can be confirmed in the situation where the size of the cluster changes or the number

of nodes in the field changes.

ACKNOWLEDGEMENTS

This research was supported by the MISP (Ministry of Science, ICT & Future Planning), Korea,

under the National Program for Excellence in SW) (2015-0-00914) supervised by the IITP

(Institute for Information & communications Technology Promotion) (2015-0-00914).

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Authors

Sanghyeok Lim Received a B.S. degree in Digital Information Engineering from

Hanguk University of Foreign Studies in 2017, and is now working toward an M.S.

degree in the Department of Electrical and Computer Engineering at Sungkyunkwan

University.

Taeho Cho Received a Ph.D. degree in Electrical and Computer Engineering from

the University of Arizona, USA, in 1993, and B.S. and M.S. degrees in Electrical

and Computer Engineering from Sungkyunkwan University, Republic of Korea, and

the University of Alabama, USA, respectively. He is currently a Professor in the

College of Information and Communication Engineering, Sungkyunkwan

University, Korea.

Documents

GEOGRAPHIC INFORMATION -BASED ROUTING ...3.1.1 Routing problems A WSN with a vast land area has few obstacles between deployed nodes, and cluster-based security protocols mainly use